1. Field of the Invention
The present invention relates generally to methods for analyzing the performance of wafer process operations run on wafer processing equipment, and more particularly, to methods for identifying variables that cause out-of-statistical-control signals and techniques for incorporating expert knowledge to ascertain the significance of such signals.
2. Description of the Related Art
In an attempt to quantify and study the effects of process conditions during wafer processing, process engineers are tasked with running numerous process runs, each with particularly set variables, and then comprehensively studying results. The set variables, as is well known, are many. For instance, variables can include chamber pressure, chamber temperature, the delivered power to one or both electrodes, electrostatic chuck clamping voltage, types of gases and flow rates, etc. In practice, therefore, data for such variables is measured and recorded as wafers are put through process runs. Databases are created to organize the data for such variables. In the analysis of such data, specific attention is paid to ascertain whether the behavior of particular variables is within an acceptable range.
Multivariate statistical process control tools are available for the monitoring of deviations between historical process runs and new process runs. These tools can statistically define normal operating behavior in a process based on historical data. Statistical projection-based techniques, such as principal component analysis (PCA), are used to produce out-of-statistical-control signals when a variable is identified as deviating out of the bounds of normal operating behavior.
As multivariate statistical process control tools accommodate analysis across a large number of variables the resulting models are very sensitive, too sensitive with respect to some variables.
Another challenge associated with using these techniques is to determine if an out of bounds signal is considered meaningful based on expert knowledge. Some variables or ranges of variable values are more critical than others. For example, once a wafer is clamped into position the clamp voltage could vary, yet still not be considered a fault, or error in the system. Generally, faults are generated when a value for a variable changes so much that it falls out-of-statistical control. So, if a value for clamp voltage is recorded as being out of an acceptable statistical bounds relative to other variables in the system, it may be flagged as a problem and an automatic fault code would be sent out halting the wafer processing.
However an expert observing the same value for clamp voltage might not be concerned with the variable deviation. For example, though the value for clamp voltage is out of the acceptable statistical bounds it could still fall in an operating range where the clamp properly holds the wafer. Unfortunately, a fault would still be registered, even though expert knowledge would deem the out of bounds signal as not warranting a fault. The end result is that reliance on pure mathematical statistical analysis will lead to false fault alarms. Nevertheless, during processing, every fault will generally lead to stoppage of wafer processing operations, thus resulting in wasted time and money.
The models generated in statistical projection-based techniques can be made more robust by incorporating large amounts of data for a particular process and by incorporating detailed information for each variable being recorded. The limitation with this approach is that during the phase when models are being built large amounts of data are not always available for the variables and the cost of experimental operation can be very impractical.
In view of the foregoing, what is needed is a method and system for incorporating expert knowledge in the identification and reduction of false fault alarms in wafer processing systems.